Data analysis is a critical, complex component of flow cytometry experiments. In this course, we demonstrate how data analysis is relevant in nearly every experimental phase. The course begins with a module on experimental design and data acquisition, in which instrument standardization, panel design, and proper controls will be discussed from a data analysis perspective. Next, a module on data visualization will cover population identification (e.g., "gating"), data analysis in the cloud, and troubleshooting based on staining patterns. Following this module, during the lunch break, vendors will be available to demonstrate software packages and answer questions. The afternoon includes sessions on reporting results (fundamental statistics, data aggregation for presentation) and the next generation of R-based tools for automated data analysis. A course packet will be provided with supplementary materials, including review articles and cytometry list discussions that address frequently asked (and complex) questions, like "how many events should be collected?" or "what is the value of isotype controls?" In sum, this course takes a fresh look at the fundamentals of data analysis and introduces cutting-edge tools for the future.

Data analysis is a critical, complex component of flow cytometry experiments. In this course, we demonstrate how data analysis is relevant in nearly every experimental phase. The course begins with a module on experimental design and data acquisition, in which instrument standardization, panel design, and proper controls will be discussed from a data analysis perspective. Next, a module on data visualization will cover population identification (e.g., "gating"), data analysis in the cloud, and troubleshooting based on staining patterns. Following this module, during the lunch break, vendors will be available to demonstrate software packages and answer questions. The afternoon includes sessions on reporting results (fundamental statistics, data aggregation for presentation) and the next generation of R-based tools for automated data analysis. A course packet will be provided with supplementary materials, including review articles and cytometry list discussions that address frequently asked (and complex) questions, like "how many events should be collected?" or "what is the value of isotype controls?" In sum, this course takes a fresh look at the fundamentals of data analysis and introduces cutting-edge tools for the future.

This continuing medical laboratory education activity is recognized by the American Society for Clinical Pathology for .5 CMLE credit. ASCP CMLE credits are acceptable for the ASCP Board of Registry Certification Maintenance Program.

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